Vector databases have become a standard piece of AI applications that need to retrieve external knowledge. RAG (Retrieval-Augmented Generation) systems, semantic search engines, enterprise assistants, and agents use embeddings to locate information by meaning and feed the model only the context it needs. The market, however, is no longer limited to a handful of specialized Vector DBs: PostgreSQL, Redis, Elasticsearch, MongoDB, OpenSearch, and Azure AI Search have also added increasingly full-featured vector capabilities.
Vector databases in 30 seconds
- A Vector DB stores and indexes embeddings to retrieve information by similarity.
- RAG uses that retrieval step to feed the model external knowledge before it generates a response.
- Pinecone, Weaviate, Milvus, and Qdrant are especially geared toward vector workloads.
- PostgreSQL, Redis, MongoDB, Elasticsearch, and OpenSearch let you combine vectors with data platforms you already run.
- Hybrid search, which blends semantic similarity with keyword matches, is becoming an especially important capability.
- Volume, latency, filters, cost, operations, and architecture matter more than popularity when choosing the technology.
The idea behind these platforms is fairly simple. An embedding model turns text, images, audio, or other content into numeric vectors. Items that are conceptually related tend to sit close together within that multidimensional space.
A query can be converted using the same model and compared against the stored vectors. The system then retrieves the closest items using metrics such as cosine similarity, dot product, or Euclidean distance.
The problem shows up when you have to do this across millions or billions of items and still deliver answers within times an interactive application can live with.
That’s where Approximate Nearest Neighbor (ANN) indexes and algorithms come in. Technologies such as HNSW or IVF drastically shrink the space that needs to be searched, in exchange for accepting certain trade-offs between speed, memory use, and recall.
Vector Search Usually Doesn’t Work Alone Anymore
The first important decision is avoiding a common oversimplification: semantic search doesn’t necessarily mean vector search alone.
A user asking “how do I recover a forgotten password” can benefit from semantic similarity, because the relevant document might talk about “resetting credentials” instead.
But there are queries where an exact match matters far more.
An invoice number, CVE-2026-67276, a product reference, or a function name can lose relevance if you rely only on proximity between embeddings.
That’s why hybrid search is on the rise.
Weaviate, for example, combines vector search with BM25F and lets you adjust the relative weight of each. OpenSearch has hybrid queries and mechanisms to normalize and fuse results. Elasticsearch recommends Reciprocal Rank Fusion (RRF) to combine lexical and vector retrieval.
Azure AI Search runs full-text and vector search in parallel and then merges the results through RRF.
Even pgvector can work alongside PostgreSQL’s native full-text search to build a hybrid layer.
This convergence is partly erasing the line between “search engine,” “database,” and “Vector DB.”
The 16 most interesting alternatives represent precisely different points along that spectrum.
| Technology | Main profile | Deployment | Hybrid search | Especially good fit for |
|---|---|---|---|---|
| Pinecone | Managed Vector DB | Cloud/serverless | Yes | Managed RAG |
| Weaviate | Vector DB | OSS + cloud | Yes | RAG and hybrid search |
| Milvus | Distributed Vector DB | OSS + cloud | Yes | Very large scale |
| Qdrant | Vector DB | OSS + cloud | Yes | Low latency and filters |
| pgvector | PostgreSQL extension | PostgreSQL | Yes | Apps already on Postgres |
| Redis | Data platform + vectors | OSS/cloud | Yes | Real time |
| FAISS | ANN library | Local | Not as a full platform | Custom engines |
| Chroma | AI infrastructure | Local/cloud | Yes | Development and RAG |
| OpenSearch | Search engine | OSS/cloud | Yes | Search + RAG |
| Elasticsearch | Search/data platform | Self-managed/cloud | Yes | Enterprise search |
| MongoDB Vector Search | Documents + vectors | MongoDB/Atlas | Yes | MongoDB apps |
| Vespa | Search + ranking | Self-managed/cloud | Yes | Complex ranking |
| LanceDB | Multimodal data + retrieval | OSS/cloud | Yes | Multimodal AI |
| Vald | Distributed ANN | Kubernetes | Vector-only | Cloud-native |
| Marqo | AI search | Cloud | Yes | Search and multimodal |
| Azure AI Search | Managed search | Azure | Yes | Microsoft-centric enterprises |
Pinecone: abstracting away the infrastructure
Pinecone represents the managed-service approach. Its serverless indexes let you store and query information without directly operating the nodes that run the infrastructure.
Its recent evolution also shows where the market is heading: a single serverless index can combine dense vectors, sparse vectors, full-text search fields, and metadata.
It’s especially appealing when the team wants to consume vector retrieval as a service and put fewer resources into operating the platform.
Weaviate: vector and hybrid search from the same system
Weaviate combines keyword search, vectors, and hybrid queries. Its hybrid search runs vector retrieval and BM25F and then fuses the results.
It also lets you adjust the alpha parameter: a value of 0 favors keywords exclusively, and 1 uses only vectors.
It’s an interesting option when relevance needs to combine meaning with exact text matching rather than betting entirely on embeddings.
Milvus: when you’re dealing with billions of vectors
Milvus is built for large-scale vector search and offers several deployment tiers.
Milvus Lite can run as a Python library for small projects; Standalone brings the platform to a single machine, while Milvus Distributed runs on Kubernetes.
Current documentation places that last architecture in scenarios ranging from hundreds of millions to tens of billions of vectors. Milvus 3.0 also extends the approach toward data living in data lakes.
Qdrant: performance, filters, and latency control
Qdrant is a vector search engine built specifically for AI and semantic-search applications.
It supports dense, sparse, and multi-vectors, plus quantization, metadata filters, and distributed deployments.
Its options for controlling searches over data that hasn’t been indexed yet are interesting for applications where keeping latency consistent matters more than immediately surfacing the very latest ingested item.
pgvector: maybe you don’t need another database
pgvector offers a pragmatic alternative: keep the embeddings inside the PostgreSQL database the application already uses.
The extension offers exact and ANN search via HNSW and IVFFlat, and supports dense, half-precision, binary, and sparse vectors.
The architectural upside is considerable. Applications that need to relate users, permissions, products, or documents to embeddings can use SQL, JOINs, ACID transactions, point-in-time recovery, and the rest of PostgreSQL’s capabilities without necessarily bringing in another storage system.
It won’t always be the highest-performing option at extreme scale, but it can be one of the simplest architectures for many projects.
Redis: vectors alongside real-time data
Redis lets you store vectors inside Hash or JSON objects and index them through Redis Search.
It currently supports FLAT, HNSW, and SVS-VAMANA indexes, KNN queries, range searches, and metadata filtering.
It’s especially interesting when Redis is already part of the application and you need to combine vector retrieval with workloads where access speed carries a lot of weight.
FAISS: a library, not a full database
FAISS deserves an important distinction. FAISS isn’t a vector database on the same footing as Pinecone or Qdrant — it’s a library for efficient search and clustering of dense vectors, developed mainly by Meta AI Research.
It has a C++ implementation with Python wrappers, plus algorithms that can use a GPU.
It’s a solid building block for custom-built engines when the team wants to control storage, persistence, metadata, and serving, but it requires building around it the components other platforms already provide.
Chroma: lowering the barrier to entry
Chroma is especially geared toward AI application development.
It lets you store embeddings and metadata, run dense, sparse, and hybrid searches, and retrieve text, images, and other modalities. It can run locally, be self-hosted, or use Chroma Cloud.
Its ease of setup makes it a common choice for prototypes and RAG applications that can later grow into larger deployments.
From Traditional Search Engines to Hybrid Platforms
The next alternatives show another market shift: engines that already handled documents or traditional search are adding vectors.
OpenSearch and Elasticsearch
OpenSearch offers vector, semantic, hybrid, multimodal, and RAG search. It can accept externally generated embeddings or produce them through models configured in its ingestion pipeline.
Its hybrid search combines lexical and semantic retrieval and can use score normalization or Reciprocal Rank Fusion.
Elasticsearch has moved in a similar direction. It can combine full-text, kNN, and sparse vectors, and use RRF or linear combinations to blend different retrieval strategies.
Both make particular sense when an organization already uses these platforms for search, observability, or large document collections, which is also why platforms like MongoDB’s own embeddings and reranking push in Atlas follows the same logic of adding retrieval to an existing data platform rather than introducing a new one.
MongoDB Vector Search: embeddings next to the document
MongoDB lets you keep embeddings alongside the data in your collections.
The $vectorSearch operator can run semantic search over indexed vectors and apply filters before returning results.
This reduces the need to keep a document database in sync with a separate Vector DB, and it’s especially appealing for applications that already use MongoDB as their primary store.
Vespa: retrieval and ranking as one problem
Vespa takes an approach more geared toward complex search systems.
Its nearestNeighbor operator can be combined with text queries, filters, and ranking profiles. Vespa also lets you use multiple ranking phases and bring in additional signals, such as popularity or machine-learning models.
That’s attractive when retrieving candidates is only the first part of the problem and the application needs very precise control over how they get ranked.
LanceDB: vectors inside a multimodal architecture
LanceDB has evolved from the classic Vector DB concept into what it calls a Multimodal Lakehouse.
Its architecture can keep source data, metadata, and embeddings inside the same table, and combine vector search, full-text search, and SQL filters.
That approach makes sense for multimodal AI, where documents, images, audio, or video aren’t simply external references tied to an embedding.
Vald: distributed ANN on Kubernetes
Vald is a distributed ANN search engine built around a cloud-native architecture.
Its deployment is tightly tied to Kubernetes and distributes components such as agents, gateways, discovery, and indexing.
It’s a more specialized alternative for teams that want to integrate distributed vector search into a container-based architecture.
Marqo: from Vector DB to AI search engine
Marqo has evolved into a search and product-discovery platform, now especially geared toward e-commerce.
Its offering combines query understanding, semantic search, multimodal models, filters, ranking, and behavioral signals.
That’s why it’s more accurate to describe it today as an AI Search platform than as a generic Vector DB.
Azure AI Search: vector search inside the Microsoft ecosystem
Azure AI Search integrates vector, text, and hybrid search as a managed service.
Microsoft lets you supply precomputed embeddings or generate them during indexing. In hybrid queries, it runs vector and full-text searches in parallel and then merges the results through RRF.
Its integration with Azure and Microsoft Foundry makes it a natural choice for organizations that already concentrate their applications, identities, and AI services there.
How to Pick a Vector DB Without Chasing Trends
There’s no universal winner.
A proof of concept with 50,000 documents has very different needs from a search engine handling 500 million products, and both scenarios are further still from a multimodal system holding billions of vectors.
The first question should be how many vectors will actually exist two or three years from now, not just how many the prototype holds today.
Then comes latency. An internal search that answers in 500 milliseconds can be perfectly acceptable. An interactive system that runs several retrievals during each agent execution may need much shorter times.
Filters matter too.
In enterprise RAG, it’s rarely enough to find the semantically closest documents. The system may need to respect user, department, date, country, product, access permissions, or confidentiality level.
Cost isn’t just about storing vectors either. You have to factor in generating and refreshing embeddings, index memory, replicas, storage, queries, data transfer, and operational work.
Finally, there’s an architectural decision that’s often worth more than any benchmark.
If a company already has all its data in PostgreSQL and needs only a few million embeddings, pgvector can avoid introducing another platform.
If it needs tens of billions of vectors, Milvus Distributed belongs to a different category altogether.
If it wants to stop operating infrastructure, Pinecone cuts down on that work.
If traditional text search remains as important as semantic search, Elasticsearch, OpenSearch, Weaviate, or Azure AI Search each make a different case.
And if the problem is dominated by images, video, and large multimodal datasets, LanceDB proposes a different architecture.
The best Vector DB isn’t necessarily the one that answers a synthetic benchmark query a few milliseconds faster.
It’s the one that delivers the level of recall, latency, and availability the application needs without turning the retrieval layer into the most expensive and complicated piece of the whole AI architecture.
Frequently Asked Questions
What is a vector database?
It’s a system that can store and index vector representations of data and retrieve them using similarity measures. It’s commonly used for semantic search, recommendations, RAG, and AI applications.
Do you need a Vector DB to build a RAG system?
Not always. A dedicated Vector DB is one option, but PostgreSQL with pgvector, Elasticsearch, OpenSearch, Redis, or MongoDB can also provide vector retrieval. Some RAG systems can even use retrieval strategies that don’t rely on embeddings at all.
What’s the difference between vector search and hybrid search?
Vector search finds content based on semantic proximity between embeddings. Hybrid search combines that signal with lexical search, typically to preserve exact matches too and improve retrieval on queries where names, codes, or specific terms matter.
What’s the best vector database for AI?
It depends on volume, latency, filters, cost, existing infrastructure, and data type. Pinecone reduces operational overhead, Milvus is built for massive scale, pgvector simplifies PostgreSQL-based architectures, and platforms like Weaviate, OpenSearch, Elasticsearch, and Azure AI Search put particular emphasis on hybrid retrieval.